In part 1, we gathered the crucial "ingredients" for our AI creation — the data. Now, transform that data into a fully functioning Large Language Model (LLM).
Explore in-depth the technical journey of neural networks, from the basic perceptron to advanced deep learning architectures driving AI innovations today.
Take a deep dive into recommendation algorithms that are crucial for internet platforms, driving user engagement and revenue, and used by major platforms.
In this post, compare three different ways to utilize OpenTelemtry Tracing and Spring Boot components: Java agent v1, Java agent v2, and Micrometer Tracing.
Application security testing is an integral part of the development process. It is aimed at revealing and addressing security issues earlier rather than later.
August 12, 2024
by Vidyasagar (Sarath Chandra) Machupalli FBCS
CORE
Discover the mechanics that make speech recognition possible. Understanding the increasingly common voice-user interface (VUI) for applied AI could give you an edge.
This article shares the architectural decisions, trade-offs, and lessons learned from building a system where every order triggers a multi-variable matching decision
Let's walk through how to use these Mistral AI models on Amazon Bedrock with Go, and in the process, also get a better understanding of its prompt tokens.
Learn more about Apache Flink, a powerful stream processing tool, for building streaming data pipelines, real-time analytics, and event-driven applications.
This summary of steps to run the PyTorch framework or any AI workload on GPUs highlights the importance of the hardware, driver, software, and frameworks.
Retrieval augmented generation (RAG) needs the right data architecture to scale efficiently. Learn how data streaming helps data and application teams innovate.
Explore the AI/ML capabilities of Snowflake, focusing on leveraging the SNOWFLAKE.ML.ANOMALY_DETECTION function to detect anomalies in superstore sales.
Dive into the concept of semi-supervised learning and explore its principles, applications, and potential to revolutionize how we approach data-hungry ML tasks.